• Linear Regression: Linear regression is one of the most fundamental algorithms in machine learning. In this lesson, learn how linear regression works.
• Perceptron Algorithm: The perceptron algorithm is an algorithm for classifying data. It is the building block of neural networks.
• Decision Trees: Decision trees are a structure for decision-making where each decision leads to a set of consequences or additional decisions.
• Naive Bayes: Naive Bayesian Algorithms are powerful tools for creating classifiers for incoming labeled data. Specifically Naive Bayes is frequently used with text data and classification problems.
• Support Vector Machines (SVM): Support vector machines are a common method used for classification problems. They have been proven effective using what is known as the 'kernel' trick!
• Ensemble Methods: Bagging and boosting are two common ensemble methods for combining simple algorithms to make more advanced models that work better than the simple algorithms would on their own.
• Model Evaluation Metrics: Learn the main metrics to evaluate models, such as accuracy, precision, recall, and more!
• Training and Tuning: Learn the main types of errors that can occur during training, and several methods to deal with them and optimize your machine learning models.
• Finding Donors (Project): After covering a wide variety of methods for performing supervised learning, now it's time to put those into action!

• Clustering: Clustering is one of the most common methods of unsupervised learning. Here, we'll discuss the K-means clustering algorithm.

• Hierarchical and Density Based Clustering: We continue to look at clustering methods. Here, we'll discuss hierarchical clustering and density-based clustering (DBSCAN).

• Gaussian Mixture Models and Cluster Validation: In this lesson, we discuss Gaussian mixture model clustering. We then talk about the cluster analysis process and how to validate clustering results.

• Dimensionality Reduction and PCA: Often we need to reduce a large number of features in our data to a smaller, more relevant set. Principal Component Analysis, or PCA, is a method of feature extraction and dimensionality reduction.

• Random Projection and ICA: In this lesson, we will look at two other methods for feature extraction and dimensionality reduction: Random Projection and Independent Component Analysis (ICA).

• Identify Customer Segments (Project): In this project, you'll apply your unsupervised learning skills to two demographics datasets, to identify segments and clusters in the population, and see how customers of a company map to them.

• Basic SQL: SQL basics for working with a single table. Learn the key commands to filter a table in many different ways.
• SQL Joins: Learn how to combine data from multiple tables together.
• SQL Aggregations: Learn how to aggregate data using SQL functions
• SQL Subqueries & Temporary Tables: Learn about subqueries, a fundamental advanced SQL topic. This lesson will walk you through the appropriate applications of subqueries, the different types of subqueries, and review subquery syntax and examples.
• Query a Digital Music Store Database (Project): Use a digital music store database to help the company see how they might optimize their business practices.

• Descriptive Statistics - Part I: Learn about data types, measures of center, and the basics of statistical notation.
• Descriptive Statistics - Part II: Learn about measures of spread, shape, and outliers as associated with quantitative data. You will also get a first look at inferential statistics.
• Admissions Case Study: Learn to ask the right questions, as you learn about Simpson's Paradox.
• Probability: Gain the basics of probability using coins and die.
• Binomial Distribution: Learn about one of the most popular distributions in probability - the Binomial Distribution.
• Conditional Probability: Not all events are independent. Learn the probability rules for dependent events.
• Bayes Rule: Learn one of the most popular rules in all of statistics - Bayes rule.
• Python Probability Practice: Take what you have learned in the last lessons and put it to practice in Python.
• Normal Distribution Theory: Learn the mathematics behind moving from a coin flip to a normal distribution.
• Sampling distributions and the Central Limit Theorem: Learn all about the underpinning of confidence intervals and hypothesis testing - sampling distributions.
• Confidence Intervals: Learn how to use sampling distributions and bootstrapping to create a confidence interval for any parameter of interest.
• Case Study: A/B tests.
• Regression: Use python to fit linear regression models, as well as understand how to interpret the results of linear models.
• Multiple Linear Regression: Learn to apply multiple linear regression models in python. Learn to interpret the results and understand if your model fits well.
• Logistic Regression: Learn to apply logistic regression models in python. Learn to interpret the results and understand if your model fits well.
• Analyze A/B Test Results (Project): You will be working to understand the results of an A/B test run by an e-commerce website. Your goal is to work through to help the company understand if they should implement the new page design.
  
  

• Introduction to Data Visualization: Learn to evaluate the quality of data visualizations and build high quality visualizations, starting with the fundamentals of data dashboards.

• Design: Learn to implement the best design practices, and to use the most appropriate chart for a particular situation.

• Data Visualizations in Tableau: Learn to build data visualizations in Tableau using data hierarchies, filters, groups, sets, and calculated fields, as well as create map-based data visualizations in Tableau.

• Make Dashboards & Stories in Tableau: In this final lesson you learn how to build interactive Tableau dashboards and tell impactful stories using data.

• Flight Delays Dashboard - (Data Visualization Project): Build interactive dashboards with Tableau and use them to discover and communicate insights from data.

• Data Types and Operators: Familiarize yourself with the building blocks of Python. Learn about data types and operators, built-in functions, type conversion, whitespace, and style guidelines.
• Data Structures: Use data structures to order and group different data types together! Learn about the types of data structures in Python, along with more useful built-in functions and operators.
• Control Flow: Build logic into your code with control flow tools! Learn about conditional statements, repeating code with loops and useful built-in functions, and list comprehensions.
• Functions: Learn how to use functions to improve and reuse your code! Learn about functions, variable scope, documentation, lambda expressions, iterators, and generators.
• Scripting: Setup your own programming environment to write and run Python scripts locally! Learn good scripting practices, interact with different inputs, and discover awesome tools.
• NumPy: Learn the basics of NumPy and how to use it to create and manipulate arrays.
• Pandas: Learn the basics of Pandas Series and DataFrames and how to use them to load and process data.
• Explore US Bikeshare Data (Project): Use Python to understand U.S. bikeshare data. Calculate statistics and build an interactive environment where a user chooses the data and filter for a dataset to analyze.

• Jupyter Notebooks: Jupyter Notebooks are a great tool for getting started with writing python code. Though in production you often will write code in scripts, notebooks are wonderful for sharing insights and data visualization.
• The Data Analysis Process: Learn about the data analysis process and practice investigating different datasets using Python and its powerful packages for data analysis.
• Gathering Data: Gather data from various sources and a variety of file formats using Python. Rotten Tomatoes ratings, Roger Ebert reviews, and Wikipedia movie poster images make up the dataset for this lesson.
• Data Analysis Process - Case Study 1: Investigate a dataset on chemical properties and quality ratings of wine samples by going through the entire data analysis process and building more skill with Python for data analysis.
• Data Analysis Process - Case Study 2: Investigate a more challenging dataset on fuel economy and learn more about problems and strategies in data analysis. Continue to build on your Python for data analysis skills.
• Investigate a Dataset (Project): Choose one of Udacity's curated datasets, perform an investigation, and share your findings.

• Introduction to Data Wrangling: Identify each step of the data wrangling process (gathering, assessing, and cleaning) through a brief walkthrough of the process. The dataset for this lesson is an online job postings dataset from Kaggle.

• Gathering Data: Gather data from various sources and a variety of file formats using Python. Rotten Tomatoes ratings, Roger Ebert reviews, and Wikipedia movie poster images make up the dataset for this lesson.

• Assessing Data: Assess data visually and programmatically for quality and tidiness issues using pandas. The dataset for this lesson is mock Phase II clinical trial data for a new oral insulin called Auralin.

• Cleaning Data: Using Pandas, clean the quality and tidiness issues you identified in the "Assessing Data" lesson. The dataset is the same: mock Phase II clinical trial data for a new oral insulin called Auralin

Big Data: Why and Where?: Data has been around (even digitally) for a while. What makes data "big" and where does this big data come from?

Characteristics of Big Data and Dimensions of Scalability: The "Big Vs". Examples and descriptions of the commonly discussed 5 Vs. Propose a 6th V, and practice writing Big Data questions targeting this V -- value.

Data Science: Getting Value out of Big Data: The reality is we care about Big Data because it can bring value to our companies, our lives, and the world. In this module we introduce a 5 step process for approaching data science problems.

Foundations for Big Data Systems and Programming: Big Data requires new programming frameworks and systems. Give you a grounding in some of the key concepts.